A Comparative Study of Sampling Analysis in the Scene Classification of Optical High-Spatial Resolution Remote Sensing Imagery

By Jingwen Hu, Gui-Song Xia, Fan Hu, Liangpei Zhang. 2015

35 citations recorded by the source corpus.

Scene classification, which consists of assigning images with semantic labels by exploiting the local spatial arrangements and structural patterns inside tiled regions, is a key problem in the automatic interpretation of optical high-spatial resolution remote sensing imagery. Many state-of-the-art methods, e.g., the bag-of-visual-words model and its variants, the topic models and unsupervised feature learning-based approaches, share similar procedures: patch sampling, feature learning and classification. Patch sampling is the first and a key procedure, and it has a considerable influence on the results. In the literature, many different sampling strategies have been used, e.g., random sampling and saliency-based sampling. However, the sampling strategy that is most suitable for the scene classification of optical high-spatial resolution remote sensing images remains unclear. In this paper, we comparatively study the effects of different sampling strategies under the scenario of scene classification of optical high-spatial resolution remote sensing images. We divide the existing sampling methods into two types: random sampling and saliency-based sampling. Here, we consider the commonly-used grid sampling to be a specific type of random sampling method, and the saliency-based sampling consists of keypoint-based sampling and salient region-based sampling. To compare their performances, we rely on a standard bag-of-visual-words model to learn the global features for testing because of its simplicity, robustness and efficiency. In addition, we conduct experiments using a Fisher kernel framework to validate our conclusions. The experimental results obtained on two commonly-used datasets using different feature learning methods show that random sampling can provide comparable and even better performance than all of the saliency-based strategies.

Subfields: Computer Vision and Pattern Recognition, Media Technology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Junwei Han
2. Gong Cheng
3. Liangpei Zhang
4. Xiaoqiang Lu
5. Gui-Song Xia
6. Xiang Bai
7. Yanfei Zhong
8. Guisong Xia

In Media Technology

1. Gong Cheng
2. Junwei Han
3. Xiaoqiang Lu
4. Liangpei Zhang
5. Gui-Song Xia
6. Yanfei Zhong
7. Xiang Bai
8. Guisong Xia

Citations in the corpus

• Remote Sensing Image Scene Classification: Benchmark and State of the Art (2017, methodology): “been widely adopted to evaluate various different scene classification methods [9, 16, 18, 33, 37, 83-87, 97].”
• AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification (2016, methodology): “6) IFK [76] uses the Gaussian mixture model (GMM) to encode local image features [98] and achieves a good performance in scene classification [22], [30], [31].”
• Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects (2021, unknown): “Some popular feature encoding or pooling strategies to enhance the performance of BoVW are Fisher vector coding [51], [73], [74], Spatial Pyramid Matching (SPM) [75], and Probabilistic Topic Model (PTM) [71], [76]– [78].”